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Bridging Language and Physics: Automated Design of Continuum Robots with Large Language Models

Jingyi Chen, Mohan Zhang, Laura Yao, Yingtai Ni, Jianmin Ji, Jie Peng, Song Wang, Tianlong Chen

arXiv:2609.08220v1cs.RO

TL;DR

LLMs struggle to design robots for complex physical interactions because language-based reasoning does not capture embodiment consequences reliably. AID-SR closes this gap through simulator feedback, semantic critique, human input, and iterative refinement, achieving high physical validity across continuum-robot tasks and transferring three designs to real-world operation.

  • Problem

    LLMs can generate robot-design representations, but their language-based reasoning poorly captures instability, collision, contact failure, and other physical consequences in complex embodied systems.

  • Method

    AID-SR combines simulator feedback, semantic critique, lightweight human feedback, iterative refinement, and constrained continuum-robot design representations.

  • Results

    The framework achieved 100% physical validity with GPT-5, while three fabricated LLM-designed robots successfully demonstrated real-world operation.

  • Takeaways & Limitations

    Layered feedback is a promising reusable strategy for grounding LLM-based embodied design and producing continuum robots that can transfer from simulation to real-world operation.

  • Takeaways & Limitations

    Simulation-to-hardware transfer remains constrained by idealized tendon routing and actuator assumptions that violate geometric, mass, thermal, or power-density limits.

Abstract

from arXiv · show

Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineffective for robots operating under complex physical interactions. This limitation stems from the gap between language-based reasoning and the physical consequences of embodiment, often resulting in designs with low physical validity. In this work, we propose a multi-layered framework, AID-SR, that establishes a closed loop by translating simulator-observed physical states into structured feedback for the LLM designer. Combined with semantic critique, human feedback, and iterative refinement, the framework promotes the generation of physically feasible and functionally meaningful robot designs. We evaluate our approach on tendon-driven continuum robots across a benchmark of 14 tasks spanning reaching, grasping, locomotion, and manipulation. The proposed framework achieves 96.2% rate for passing the simulation feasibility check and by applying a common reinforcement learning training, 26.7% robots can successfully fulfill the corresponding task. We then fabricate three designed robots of AID-SR that successfully complete the task in real-world. These extensive experiments across simulation and real-world environments demonstrate and break the wall of utilizing the LLMs for automated design of continuum robots. The source code and experimental resources are publicly available at https://github.com/UNITES-Lab/AID-SR.

I. INTRODUCTION

LLMs can generate robot designs from high-level specifications, but language-based reasoning does not reliably capture physical consequences in complex interactions. AID-SR addresses this gap with iterative, feedback-driven design and evaluates it on continuum robots across simulation and real-world tasks.

  • Automated robot design traditionally depends on expert intuition and labor-intensive trial-and-error for systems involving deformation, contact, and nonlinear dynamics.
  • LLM-generated designs often lack physical validity because instability, self-collision, and contact failure are difficult to represent exhaustively through language.
  • AID-SR exposes the LLM iteratively to simulator-observed physical states, semantic critique, and human feedback while refining robot morphology and physical parameters.
  • The framework targets tendon-driven continuum robots, whose high degrees of freedom, contact behavior, and sensitivity to geometry, materials, and tendon routing challenge design automation.
  • A benchmark of 14 tasks spans reaching, grasping, locomotion, and manipulation, with reinforcement learning applied to designs that pass physical validation.
  • The work contributes a physically grounded framework with lightweight human feedback and demonstrates simulation-based design and sim-to-real transfer for tendon-driven continuum robots.

II. RELATED WORK

Prior automated robot-design methods rely on predefined structures, learned design datasets, or LLM-generated code, but remain constrained in expressiveness, training data, or physical-task complexity. Continuum robots provide a structured yet rich testbed for language-based design.

  • Parameter-optimization methods depend on human-specified design spaces, while diffusion-based methods depend closely on training-data quality and diversity.
  • LLM-based robot design has commonly used idealized 2D environments or relatively simple 3D crawling robots, limiting physical and task complexity.
  • Continuum robots use compliant backbones for continuous deformation, supporting adaptability and safety in contact-rich applications such as surgery and confined-space manipulation.
  • Discretized backbone models provide a practical trade-off between physical fidelity and computational efficiency for simulation and learning-based control.
  • Segmented-backbone continuum robots retain design flexibility, enabling diverse morphologies from combinations of rigid segments and tendons.
  • Their symbolic and parametric representation makes continuum robots suitable for language-based design while retaining high-dimensionality and complex contact behaviors.

III. METHOD

The paper formulates autonomous robot design as generating physically stable and functionally promising MuJoCo scenes from a natural-language objective and environment specification. A direct one-shot LLM baseline performs poorly, motivating structured validation and refinement.

  • AID-SR is evaluated through an agentic pipeline that combines initial design conception, iterative feedback, continuum-robot design constraints, and reinforcement-learning-based task evaluation.
  • The design objective is to generate multiple valid MuJoCo scenes from a natural-language objective and environment specification.
  • Each generated scene must contain a physically stable continuum-robot morphology capable of learning a control policy for the objective.
  • A direct GPT-5 one-shot baseline achieved nearly 0% success in producing syntactically correct and physically stable files.
  • The baseline failed through invalid XML, unstable structures that collapsed or exploded, and task-irrelevant morphologies, motivating validation and iterative refinement.

B. The AID-SR Framework: An Agentic Workflow for Soft Robot Design

AID-SR decomposes robot design into specialized agents connected by a closed-loop refinement process. Multiple concepts are converted into robot files, checked for physical and task compatibility, semantically judged, optionally reviewed by a human, and forwarded for downstream evaluation.

  • Agentic workflow: AID-SR uses specialized agents in a closed-loop system to decompose complex design and systematically explore the design space.
  • Initial design conception: The Designer agent proposes several distinct high-level concepts to preserve design diversity and reduce premature convergence.
  • Hybrid feedback and refinement loop: The refinement loop integrates four feedback layers: physical stability validation, scene integration, semantic critique, and human-in-the-loop refinement.
  • Physical Stability Validation: Physical Stability Validation deterministically tests generated designs in MuJoCo and detects syntax or invalid-model failures before later stages.
  • Scene Integration: Scene Integration combines the robot, environment, and objective into a unified scene while adjusting task-specific compatibility details such as scale and placement.
  • Semantic critique: The LLM Judge checks whether validated integrated designs are coherent and task-meaningful, returning failed designs with structured critiques for revision.
  • Human-in-the-loop refinement: Human engineers may provide high-level natural-language feedback after automated validation, although the pipeline can terminate without human intervention.
  • Downstream evaluation: Qualified designs proceed to reinforcement-learning experiments, while repeated failures are terminated after an imposed iteration bound.

C. Design Space Definition

The framework defines tendon-driven continuum robots as under-actuated, passively compliant soft structures and evaluates their functional quality with standardized reinforcement learning. Designs are generated from task descriptions, verified in simulation, and assessed for structural validity and task success.

  • Design space: Tendon-driven continuum robots use multiple joint-connected body segments and tendons to produce global shape deformation through under-actuated passive compliance.The design space includes ball or hinge joints, primitive geometries, actuator layouts, actuator types, and control parameters.
  • Design generation: The LLM receives a brief task description, explains its mechanical rationale, and generates a complete XML design for simulation verification and refinement.The framework provides code examples and MuJoCo modeling specifications to support syntactically valid XML generation.
  • Evaluation: Functional quality is evaluated through a standardized downstream reinforcement-learning pipeline using PPO across designs.Task-specific observation, action, reward, and success formulations differ across benchmark objectives, while the learning algorithm remains consistent.
  • Evaluation: The evaluation aims to test each morphology’s task potential with basic, uniform training rather than optimize every design extensively.This standardization demonstrates whether generated morphologies can accomplish their tasks under comparable training conditions.
  • Validity criteria: A design is valid only if it completes training without implausible behavior or simulator failure and passes manual inspection as a structurally plausible continuum robot.Validity is distinct from satisfying the task-specific success criterion during evaluation.

IV. EXPERIMENTS

The benchmark evaluates LLM-generated soft-robot designs across 14 subtasks in four categories: reaching, grasping, locomotion, and complex manipulation. Scenario variations test robustness and generalization across distinct deformation, interaction, and motion requirements.

  • Benchmark structure: The benchmark contains 14 subtasks organized into reaching, grasping, locomotion, and complex-manipulation categories, with multiple scenario variations per subtask.The variations are used to assess robustness and generalization.
  • Reaching: Reaching requires functional body parts to reach specified target points within environments typically comparable to the robot’s own scale.The task tests whether the LLM can design purposeful soft-robot deformation.
  • Grasping: Grasping requires a soft gripper to pick up an object, maintain a stable hold, and deliver it to a target location.The benchmark excludes the subsequent placement phase and focuses on the end-effector’s design.
  • Locomotion: Locomotion requires the entire robot body to travel through an environment to a destination far beyond its own body scale.This evaluates whether the design can generate and sustain overall motion.
  • Complex manipulation: Complex manipulation includes repeatedly opening and closing three vertically arranged drawers and brushing an entire wall while controlling contact force.These tasks combine sophisticated operation sequences with mechanical and interaction requirements.

B. Experimental Setup

The experiments combine MuJoCo-based simulation, task-specific reinforcement-learning formulations, and iterative human inspection through a web interface. Designs are evaluated with validity, success, and token-efficiency metrics under a consistent AID-SR protocol.

  • Human-in-the-loop: The web interface supports environment setup, MuJoCo and agent monitoring, and approve-or-refine decisions with manual feedback.The interface exposes logs and generated XML artifacts for iterative steering.
  • Simulation and learning: AID-SR uses MuJoCo 3.3.4 through Gymnasium 1.1.1, with hybrid observations combining proprioception and task-specific features.Rewards are customized per task, and PPO from Stable Baselines3 is used for downstream training.
  • Task formulations: Reaching and locomotion use unconstrained exploration, while button-pushing, surface adapting, and pipe insertion use rewards tailored to deformation, target proximity, or contact force.These formulations adapt the learning signal to the task interaction.
  • Task formulations: Symmetric grasping actuators are grouped and controlled jointly when the design rationale identifies paired finger structures.This creates structured actions aligned with the intended grasping mechanism.
  • Task formulations: Drawer opening and wall brushing use sequential reward shaping for operation stages or trajectory following with force maintenance.Drawer rewards progress through approach, grasp, and pulling; brushing rewards trajectory tracking within a desired force range.
  • Metrics: The evaluation reports Valid Rate, Success Rate, Average Token Count, Average Cost, and Average Latency.Main results use AID-SR without human feedback to maintain evaluation consistency.

C. Framework Performance on Benchmark Tasks

Across benchmark tasks, AID-SR generates syntactically valid designs, but only a small fraction successfully complete tasks after training. Performance is strongest for reaching and declines for grasping and complex manipulation, while generation averages 151K tokens and $0.73 per design.

  • Overall performance: Only a small fraction of generated designs successfully complete tasks despite efficient generation of syntactically valid design files.The result reflects the remaining limitations of current LLMs in producing functionally successful robots.
  • Category differences: Reaching achieves the highest success rate because relatively simple structures can provide sufficient workspace and range of motion.Locomotion follows, but requires coherent global motion and lacks a scalable morphology-aware reward scheme.
  • Category differences: Grasping performs worse because the LLM rarely generates complete, well-fitted, and structurally stable soft grippers.The cited explanation attributes the difficulty to limited understanding of global morphology and physical contact.
  • Category differences: Complex manipulation exposes current LLM limitations through sophisticated task sequences and corresponding mechanical requirements.These tasks demand more than syntactically valid scene generation.
  • Efficiency: 151K tokens, $0.73, and 523s are the average token count, cost, and latency per design across all 14 tasks.Most tasks fall within roughly 100–185K tokens, $0.54–$0.88, and 411–617s.

D. Ablation Studies and Component Analysis

The ablations show that AID-SR’s hybrid feedback components contribute to task performance and physical validity, while larger backbone models better support iterative correction.

  • The full framework achieves the highest overall performance across the ablation configurations.
  • Removing the Judge reduces task success by 10.7%–20% compared with the full framework without human feedback.The result indicates that semantic guidance supports meaningful functional improvement during refinement.
  • The No Scene Integrator and No Judge or Scene Integrator variants show degraded performance, underscoring environment-aware evaluation and component composition.
  • The No Validation variant achieves a 0% physical validity rate because its designs cannot be reliably instantiated or simulated in MuJoCo.
  • GPT-5 achieves 100% physical validity and the best overall task performance among GPT-5, Gemini 2.5 Pro, and Claude Sonnet 4.5.The framework prioritizes physical validity because task success has higher variance from downstream training stochasticity.
  • Smaller-capacity open-source backbones frequently fail to recover from invalid designs and remain trapped in repeated infeasible generations.The analysis suggests that sufficient model capacity is important for dense constraints and multi-step correction.

E. Sim2Real Demonstration and Discussion

Three high-performing simulated designs were fabricated and evaluated on the same tasks, showing visual and functional sim-to-real consistency while exposing important hardware-realizability limits.

  • Reward differences in the reinforcement-learning curves do not directly indicate LLM or framework performance because individual robot designs are uncertain and random.The figure primarily illustrates reinforcement-learning effectiveness and stability.
  • Three high-performing designs were reconstructed from their design files, fabricated using 3D printing, and evaluated on the same tasks as in simulation.
  • The simulated and real executions exhibit a high degree of visual and functional consistency despite modeling inaccuracies and unmodeled physical effects.The comparison includes overall motion patterns, deformation behaviors, and task execution strategies.
  • The defined design space has meaningful physical practicality, with key parameters largely within regimes realizable through common fabrication methods and commercially available components.
  • Simulated tendons may bypass intermediate body parts and simulated actuators may assume impractical gear ratios, power densities, and missing size, mass, or thermal constraints.These discrepancies expose a sim-to-real gap and motivate more restrictive modeling of actuation and transmission mechanisms.
  • The successful deployment of three representative designs demonstrates that LLM-generated morphologies can perform meaningful real-world tasks beyond idealized simulation environments.

V. CONCLUSION

AID-SR offers a reusable, feedback-driven pathway for grounding LLMs in physical robot design, with demonstrated transfer from simulation to real-world operation. However, generated robots remain relatively simple, and the framework does not yet provide task-level performance feedback.

  • AID-SR grounds LLM-based robot design through layered interaction with constraints, simulation feedback, and semantic critique.
  • The framework produces tendon-driven continuum-robot designs that transfer from simulation to real-world operation.
  • Most generated robots remain relatively simple because of simulator limitations and the LLM’s developing ability to propose unconventional yet reasonable designs.
  • The framework guides valid designs but lacks feedback mechanisms for task-level performance.
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